In medical imaging, image segmentation is one of the most important tasks of surgical persons to obtain in-depth analysis of medical images. The goal of image segmentation is to identify a region of interest (ROI) and to highlight the boundary of the ROIs so that an operator carrying out image analysis is able to tell the ROIs from the rest of the image content.
There exist numerous tours for image segmentation. For instance, automatic processing is advantageous when it is applied to well defined structures and standard contouring definitions. However, this technique is sometimes unattainable due to limitations such as image acquisition errors, abnormalities in the image content and the presence of local image ambiguities. In order to overcome the afore-mentioned drawbacks of image segmentation based on pure automatic image processing, a number of approaches to incorporate user information into automatic image processing have been developed. In particular, user inputs are involved in contour correction in order to speed up and simplify the delineation of structures contained in the medical images.
There exist several main types of inputs which can be provided by the user during the interactive segmentation process. For instance, the user may set the value for one or more segmentation parameters such as the threshold level for binarization, weighting factors in a cost function of the deformable model, quality level used to define a quality criterion by an objective function, etc.
Other approaches enable the user to draw an initial contour around a target structure and to adjust the initial contour by improving its matching to the target structure. This can be done using one or more algorithmic models known in the art, including active contour, graph cut, elastic contour model and model-based segmentation, etc.
There exist also interactive segmentation tours which take user-initiated motions into account. When a user draws the initial contour, he usually sets an image positioner means such as a mouse into motion. This user-initiated motion is then translated into an initial contour. One example of these approaches is the life lane method where the mouse speed is used as an indication of local image quality in order to dynamically calibrate weights in a cost function. However, the approaches known in the art are limited in their accuracy and efficiency since the information about user-initiated motions is not effectively applied to speed up a process of image segmentation.
In addition, the delineation of structures on 3D medical images slice by slice is tedious and time consuming. Though current delineation tools allow the user to outline a structure or fill it from within, the required accuracy of the user is rather high. Some smart tools snap to image gradients, but the delineation still requires a lot of precise mouse movements. The planning of intensity modulated radiotherapy requires delineation of risk structures in the planning CT. Often, this is done manually and the user has to outline the structure carefully with a contouring tool, which is very time consuming. To outline all structures for a head & neck RT planning case might take up to 10 hours. There are some image processing techniques to aid this process: For example the lung can be contoured by setting a threshold and a seed point. However, this strategy only works for very few structures. Automated or semi-automated contouring methods exist, but still need to be corrected or re-done for many cases. Further, many algorithms require prior knowledge on structures to contour from a library. Thus, for many known structures and all new or uncommon structures, still a lot of time is needed for accurate delineation.
In “Olabarriaga et al., Interaction in the segmentation of medical images: A survey”, Medical Image Analysis 5 (2001) 127-142, existing interactive segmentation methods are discussed with respect to the aspects including the type of user input, how the user input affects the computational part of the segmentation process and the purpose of user interaction.